Selective Nonoperative Management in 1106 Patients With Abdominal Gunshot Wounds
Bibliographic record
Abstract
In Brief Objective: The primary aim of this study was to delineate the role of computed tomography (CT) in patients undergoing NOM for AGSW. Background: Nonoperative management (NOM) of abdominal gunshot wounds (AGSWs) remains controversial. Methods: This prospective study included all patients with abdominal gunshot injuries admitted to our trauma center from April 1, 2004 to September 30, 2009. Exclusion criteria included patients with peritonitis, hemodynamic instability, unreliable physical examination, head and spinal cord injury with an AGSW underwent immediate laparotomy. The remaining patients were selected for NOM. Nonperitonitic stable patients with right-sided thoracoabdominal/right upper quadrant gunshots and/or hematuria underwent mandatory CT with intravenous contrast. CT to detect missile trajectory was optional. The primary outcome measure was failure of NOM. Secondary outcomes were unnecessary laparotomy rates and mortality. Results: A total of 1106 patients with abdominal gunshot injuries were admitted. Of these, 834 (75.4%) underwent immediate laparotomy, whereas 272 (24.6%) were selected for NOM. In the former group, there were 56 (6.7%) deaths and 29 (3.5%) unnecessary laparotomies, whereas in the latter NOM group, 82 (30.1%) patients were managed by serial clinical examination alone, whereas 190 (69.9%) patients underwent abdominal CT scanning, in addition to serial clinical examination. The overall NOM success rate was 95.2%. Of the 13 patients undergoing delayed laparotomy, there were 10 therapeutic, 2 nontherapeutic, and 1 negative laparotomy. Conclusions: The NOM of appropriately selected patients with AGSW with selective use of CT scanning is feasible, safe, and effective, but largely based on findings from serial clinical examinations. Clinical examination is critical and accurate at predicting patients requiring laparotomy after abdominal gunshot wounds (AGSWs). Nonoperative management (NOM) of AGSWs in patients without diffuse peritonitis and persistent hypotension, and a reliable serial physical examination, is safe. The selective use of computed tomographic scanning in patient candidates for NOM is also safe.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".